By Venkata Ramana Nethi CSCP, CPIM, Business Process Lead - Operations, Schlumberger
The Association for Supply Chain Management defines level-1 metrics as diagnostics for the overall health of the supply chain. The Association says that level-1 metrics are also referred to as strategic metrics and KPIs. According to the definition, benchmarking level-1 metrics or KPIs helps establish realistic targets to support strategic goals.
Selecting performance measures to calculate your KPIs
Multiple performance measures make up KPIs, so identifying the appropriate performance measures will have an impact on the effectiveness of your KPIs. Performance analysis is one method that can be used to select performance measures. This method is a good starting point for making effective decisions to gain a competitive advantage (Figure 1).
Performance Analysis --> Decision Making --> Market Response --> Competitive Advantage
Figure 1: Performance Measures to Gain Competitive Advantage Flow
During the selection of your performance measures, consider the following:
- What to Measure?
- How to Measure?
- How to Interpret?
Perfor Performance measures can be categorized into three types:
- Lagging: Historical look at past performance (i.e., quarterly revenue, hours off due to job related injury, or employee turnover)
- Leading: Predictive of future results (i.e., safety training completion rate, scheduled maintenance compliance rate, or grievances)
- Real-time: Where things are right now (i.e., inventory levels, employee count, or contractual obligations outstanding)
Performance measures should be compared to benchmark target values. The following are signs of an effective performance measure:
- Tracks mission critical outcomes and activities
- Influenced by productive behavior but can’t easily be manipulated by your team
- Something that all team members can focus their efforts on
- Value of measuring exceeds cost of measuring
KPIs should be classified based on the purpose and users. Table 1
provides a breakdown of the three different types of KPIs.
6 months to 5+ years
forward looking based on historical and market data
CEOs, shareholders and board of directors
Typical KPIs include obsolete product mix (sales trends), (ROE/ROI), weighted average cost of capital (WACC), market and customer segmentation
1 to 12 months
Senior and middle management
Ideal KPIs include production data, vendor performance, monthly and quarterly reports, division profit and loss KPIs, inventory, employee data, etc.
1 to 30 days (Changes in outside tolerances require immediate action.)
Line managers and operators
Typical KPIs include shipments, production plans, transportation, customer complaints, stock requirements, capacity management.
Table 1: Three Types of KPIs
Examples of Effective KPIs
Figures 2 – 7 showcase KPIs that have proven effective for the different domains in the value chain, regardless of the industry.
Note: These should be considered for reference purposes only. Management analysts should use this list to lead discussions within their domain and narrow down what matters in their own organizations.
Plan/ Design Products/ Services
Figure 2: Example of KPIs – Plan/Design Products/Services
Figure 3: Example of KPIs – Perform Procurement
Figure 4: Example of KPIs – Produce Products/Services
Distribution of Products
Figure 5: Example of KPIs – Distribution of Products
Marketing & Sale of Products/Services
Figure 6: Example of KPIs – Marketing & Sale of Products/Services
Manage Customer Service
Figure 7: Example of KPIs – Manage Customer Service
Case study: Implementing SAP Business Intelligence on SAP HANA to enhance decision-making capabilities
An organization must have a sustainable and scalable toolset to support the volume of data from a company’s ERP system that feeds KPIs in their respective domains, and to enhance strategic reporting and improve the analytic and decision-making process. The following case study provides an example of an organization that enhanced their KPI support capabilities by implementing SAP Business Intelligence (BI) on HANA.
The organization first implemented SAP Business Information Warehouse (SAP BW) reporting as a means to support their KPIs in 2012. The organization’s SAP BW system supported global management and operational reporting for sales, finance, supply chain, quality, engineering and manufacturing functional areas using SAP BW version 7.4 and analytical modeling on DB2 (~ 6.6 TB in size). Challenges discovered included performance issues with data loads impacting BPC and the month-end close process, execution of business queries and reports, and sub-optimal user experience. In search of a more sustainable method, the organization prepared a business case to enhance their capabilities using SAP Business Intelligence (BI) on HANA in 2019. Figure 8 provides a high-level view of data flow.
Figure 8: High Level Landscape Showing Data Flow to Generate KPIs / Dashboards
Highlights of the migration
- BW on SAP HANA migration cycle and milestones were defined within 6-month timeline.
- The customer documented the performance improvement before and after migration for both data loads and report execution and improvement recommendations were applied.
- The customer prepared the model for the end-user experience before the system went live and identified performance deficiencies that might negatively impact user productivity and experience.
- They also discovered performance flaws in the application and supported infrastructure in a controlled manner. The flaws were addressed early on through additional sizing and other measures.
The migration to SAP BI on SAP HANA provided improved integration, performance, scalability, and flexibility, and enabled the company to address their current and future business needs, providing a technology foundation that will enable the organization to improve the efficiency of IT processes, deliver more advanced capabilities, and ultimately improve the user experience and customer satisfaction. Figure 9 shows the following process chain data loading performance improvements:
- Performance for most of the loads which were within BI improved by 25 to 70%
- Major improvement in terms of data store object (DSO) activations, which improved by almost 80%
Figure 9: Performance Analysis Pre- and Post-Migration to SAP BW on HANA
Prior to the SAP HANA migration the company experienced poor performance on data loads and reporting, due to huge volumes of data. After the upgrade to SAP HANA, users reported that performance increased 30% to 80% for the same data counts, as shown in Figure 10.
Figure 10: Performance Analysis of KPI Reporting from Users
What does this mean for SAPinsiders?
SAPinsiders can use the guidelines in this article to identify KPIs in their organization. Information mentioned is applicable to small-, medium- and large-sized organizations. Based on these guidelines, SAPinsiders should:
- Allow competitive advantage to be the driver. Research indicates that the value drivers of the organization’s success should determine the KPIs. One size does not fit all, so KPIs will be different for a manufacturing vs. a service firm, for example.
- Identify the stakeholders. Senior management should drive KPI projects and IT should offer support. Make sure the data and formulas used to arrive at the values in terms of percentage, dollar, or frequency are aligned to the strategic goals.
- KPIs should result in actionable outcomes. Research indicates that the measures should be divided into strategic, informational, and operational categories with different levels of aggregation of data with time horizons, which should aid people at various levels in the organization. KPIs are level-1 metrics and drive multiple action items at the individual level so consider carefully which add value in your business’s unique decision-making process.
- Educate yourself on the process. Successful organizations explore benchmarking reports available in the market, set challenging but achievable targets, and involve outside experts from the industry to define the relevant KPIs, as these factors impact the bottom line. Organizations should not measure something just because data is easily available. Instead, break down the value chain into key sub-processes and then do performance analysis that supports strategic goals.